Bujnicki’s research combines bioinformatics, structural biology, and synthetic biology. His scientific achievements include the development of methods for computational modeling of protein and RNA 3D structures, discovery and characterization of enzymes involved in RNA metabolism, and engineering of proteins with new functions. In the context of the EPITRAN project, it is essential to mention the development of MODOMICS, a database of RNA modification pathways, and experimental as well as theoretical studies on RNA modification enzymes, in particular RNA methyltransferases.

1. Smietanski M, Werner M, Purta E, Kaminska KH, Stepinski J, Darzynkiewicz E, Nowotny M, Bujnicki JM. Structural analysis of human 2-O-ribose methyltransferases involved in mRNA cap structure formation. Nature Commun 2014, 5:3004, doi:10.1038/ncomms4004. We determined the structures and the mechanisms of actions of CMTr1 and CMTr2, using X-ray crystallography in combination with computational modeling. Our results revealed how the human methyltransferases recognize and chemically modify the cap structure and that they do it differently from viral enzymes, thereby providing a framework for targeting these viral proteins by drugs that are safe for humans.
2. Boccaletto P, Machnicka MA, Purta E, Piatkowski P, Baginski B, Wirecki TK, de Crécy-Lagard V, Ross R, Limbach PA, Kotter A, Helm, Bujnicki JM. MODOMICS: a database of RNA modification pathways. 2017 update. Nucleic Acids Res. 2018 Jan 4;46(D1):D303-D307 MODOMICS is a database system that stores information about RNA modification pathways, enzymes involved, and sequences of RNA molecules containing modified residues. It is very popular in the RNA modification community, it has been periodically updated and the updates have been published in a series of articles, which together have been cited  >700 times.
3. Rother M, Rother K, Puton T, Bujnicki JM, ModeRNA: A tool for comparative modeling of RNA 3D structure, Nucleic Acids Res 2011 May 1;39(10):4007-22.  We developed the first fully automated method for comparative modeling of RNA 3D structures. Among unique features of ModeRNA is the ability to model not only the standard A, U, C, G, residues, but also >100 residues resulting from posttranscriptional modifications.
4 Boniecki MJ, Lach G, Dawson WK, Tomala K, Lukasz P, Soltysinski T, Rother KM, Bujnicki JM SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction Nucleic Acids Res 2016;44(7):e63. We developed a new method for the modeling of RNA 3D structures that does not require any “templates” of known structure. SimRNA has been evaluated and validated in the RNA Puzzles competition, and was found to pgenerate very accurate models.  
5. Glow D, Pianka D, Sulej A, Kozlowski L, Czarnecka J, Chojnowski G, Skowronek KJ, Bujnicki JM, Sequence-specific cleavage of dsRNA by Mini-III RNase Nucleic Acids Res 2015;43(5):2864-73. We found that RNase Mini-III from Bacillus subtilis (BsMiniIII) exhibits sequence-dependent cleavage of long dsRNA. BsMiniIII may serve as a prototype of a sequence-specific dsRNase that could possibly be used for targeted cleavage of dsRNA. The publication has been awarded the status of a Breakthrough Article.
6. Glow D, Kurkowska M, Czarnecka J, Szczepaniak K, Pianka D, Kappert V, Bujnicki JM, Skowronek KJ; Identification of protein structural elements responsible for the diversity of sequence preferences among Mini-III RNases; Sci Rep. 2016;6:38612. As a follow up of the BsMiniIII study, we analyzed 8 different MiniIII family members, demonstrated that they have different sequence preference, and successfully engineered variants with altered substrate specificities.
7. Piatkowski P, Jablonska J, Zyla A, Niedzialek D, Matelska D, Jankowska E, Walen T, Dawson WK, Bujnicki JM; SupeRNAlign: a new tool for flexible superposition of homologous RNA structures and inference of accurate structure-based sequence alignments. Nucleic Acids Res. 2017;45(16):e150. SupeRNAlign is a new computational method for comparison of RNA 3D structures, which enables superposition with conformational changes. It performs better than other methods that enable only rigid-body comparisons and generates more accurate, biologically reasonable RNA sequence alignments.
8. Philips A, Milanowska K, Lach G, Bujnicki JM; LigandRNA: computational predictor of RNA-ligand interactions RNA 2013;19(12):1605-16. LigandRNA is a computational method for predicting the 3D structure of RNA complexed with small moleucle ligands.
9. Walen T, Chojnowski G, Gierski P, Bujnicki JM; ClaRNA: a classifier of contacts in RNA 3D structures based on a comparative analysis of various classification schemes. Nucleic Acids Res. 2014;42(19):e151. ClaRNA is a new method for classyfing noncanonical interactions in RNA 3D structures .
10. Chojnowski G, Walen T, Piatkowski P, Potrzebowski W, Bujnicki JM, Brickworx builds recurrent RNA and DNA structural motifs into medium and low-resolution electron density maps. Acta Crystallogr D Biol Crystallogr 2015;71(Pt 3):697-705. RNABrickx is a new computational method for the crystallographic determination of RNA 3D structures from medium- and low-resolution electron density maps.